An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

Voelkel, Michael A.; Sachs, Anna‐Lena; Thonemann, Ulrich W.

doi:10.1016/j.ejor.2019.08.035

Cited by 19 publications

(9 citation statements)

References 34 publications

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“…Voelkel et al. (2020) complement this work by analyzing the impact different costly tracking possibilities—always, never, or dynamic—have on ordering decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Inventory control in production–inventory systems with random yield and rework: The unit‐tracking approach

Berling

Sonntag

2022

Production and Operations Management

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This paper considers a single-stage make-to-stock production-inventory system under random demand and random yield, where defective units are reworked. We examine how to set cost-minimizing production/order quantities in such imperfect systems, which is challenging because a random yield implies an uncertain arrival time of outstanding units and the possibility of them crossing each other in the pipeline. To determine the order/production quantity in each period, we extend the unittracking/decomposition approach, taking into account the possibility of order-crossing, which is new to the literature and relevant to other planning problems. The extended unit-tracking/decomposition approach allows us to determine the optimal base-stock level and to formulate the exact and an approximate expression of the per-period cost of a base-stock policy. The same approach is also used to develop a state-dependent ordering policy. The numerical study reveals that our state-dependent policy can reduce inventory-related costs compared to the base-stock policy by up to 6% and compared to an existing approach from the literature by up to 4.5%. From a managerial perspective, the most interesting finding is that a high mean production yield does not necessarily lead to lower expected inventory-related costs. This counterintuitive finding, which can be observed for the most commonly used yield model, is driven by an increased probability that all the units in a batch are either of good or unacceptable quality. K E Y W O R D Sbase-stock policy, inventory control, random yield, rework, unit-tracking approach INTRODUCTIONWe consider a single-stage make-to-stock productioninventory system under random demand and random yield, where defective units are reworked. Random yield refers to the number of items meeting the desired quality requirements, set either by a company itself or externally, for example, by the US Food and Drug Administration (FDA) for pharmaceutical products or the Federal Communications Commission (FCC) for electronic products. Our goal is to determine the ordering policy that minimizes the total average inventory-related cost per period, comprised of holding costs for all units on stock and backorder costs for all units that cannot be satisfied immediately from stock on hand.

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“…Voelkel et al. (2020) complement this work by analyzing the impact different costly tracking possibilities—always, never, or dynamic—have on ordering decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Inventory control in production–inventory systems with random yield and rework: The unit‐tracking approach

Berling

Sonntag

2022

Production and Operations Management

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“…The popularity of dynamic programming can be seen from the fact that Google Scholar's search with the keyword “dynamic programming” produced more than 100 articles in the first 4 months of the year 2020. Some examples of dynamic programming applications are lameness detection in dairy herds (Kanyamattam et al, 2020 ); reservoir operation (Rani et al, 2020 ); peak detection in Genome data (Hocking et al ( 2020 )); power optimization (Xu et al, 2020 ); periodic review model (Voelkel et al, 2020 ); aircraft maintenance scheduling problem (Deng et al, 2020 ); and aeromedical evacuation dispatching (Robbins et al, 2020 , Summers et al, 2020 ). These studies confirm that the dynamic programming approach indeed has a wide variety of applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal multi-stage group partition for efficient coronavirus screening

et al. 2022

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The outbreak of COVID-19 has affected the economy worldwide due to entire countries being on lockdown. This has been highly challenging for governments facing constraints in terms of time and resources related to the availability of testing kits for the virus. This paper develops an optimal method for multiple-stage group partition for coronavirus screening using a dynamic programming approach. That is, in each stage, a group of people is divided into a certain number of subgroups, each will be tested as a whole. Only the subgroup(s) tested positive will be further divided into smaller subgroups in the next stage or individuals at the last stage. Our multiple-stage group partition scheme is able to minimize the total number of test kits and the number of stages. Our scheme can help solve the test kit shortage problem and save time. Finally, numerical examples with useful managerial insights for further investigation are presented. The results confirm the advantages of the multi-stage sampling method over the existing binary tree method.

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“…The aim of the paper by (Aschauer et al, 2015) is to introduce a dynamic model of relationships between logistic strategies and cargo transport. A dynamic model of damage to goods during transport from a supplier to the client is described in (Voelkel et al, 2020). The authors (Chaudhary et al, 2018) analysed 419 of research papers concerning the issue of perishable goods storage in order to sum up its current situation and identify the directions of possible future development.…”

Section: Dynamic Models In Economics and Business Managementmentioning

confidence: 99%

Modeling of Time Delayed Processes in Business Economics

BOBALOVÁ¹,

NOVOTNÁ²

2022

Proceedings of the International Management Conference

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The growth of mutual dependence and interconnection of markets of both national economies and multinational groups in the form of globalization processes is a typical characteristic of the current economic development. The turbulent environment of rapidly developing markets creates conditions for an increase in competition among econ tX )( omic entities, which is one of the main reasons for better allocation of factors of production and more efficient manufacturing, leading to improved competitiveness in the global market. In business practice, there are a number of phenomena characterized by changes and dynamics. Understanding the dynamics of these changes is crurial for the management decision-making since consumer behaviour changes in time these days. Therefore, we may perceive a company in a given context as a dynamic system with multiple elements and connections. In the business environment, we may consider production, consumption, investments and others to be static variables. A model of such a dynamic system is usually based on a system of differential equations describing changes of the static vector in time. This type of equations has been increasingly used in modelling of changes and motion in various scientific fields.

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An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

Cited by 19 publications

References 34 publications

Inventory control in production–inventory systems with random yield and rework: The unit‐tracking approach

Inventory control in production–inventory systems with random yield and rework: The unit‐tracking approach

Optimal multi-stage group partition for efficient coronavirus screening

Modeling of Time Delayed Processes in Business Economics

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